Welcome to the data analysis of the General Cognitive Flexibility Scale (GCF or nflex). Here, you’ll find everything that was done to assess if the scale is suitable for futur use. Using R Markdown and the flexdashboard package, we provide all the graphs, table and analysis that was executed, along with a dataset of simulated data to reproduce the same analysis.
All participant gave their agreement for us to use those data, as long as they couldn’t be identified by any means.
The dataset contained in this folder is setup as follow :
- A row = an observation
- A column = a variable
a. All analysis performed with relevent information and statistics
b. Steps for scale construction and validation
c. Reference
d. Dataset:
1. Age,
2. Sexe,
3. Professionnal situation,
4. Response to the Autism spectrum quotien scale’s subset named attention switching also named att_switching_X in the dataframe (Baron-Cohen et al., 2011)
5. Response to the cognitive flexibility scale named cfs_X in the dataframe (Martin & Rubbin, 1995)
6. Response to the Resistance to Change Scale’s Subset named Cognitive Rigidity also named cognitive_rigidity_X in the dataset (Oreg, 2003)
7. Response to the General Cognitive Flexibility Scale also named nflex_X in the dataframe (work-in-progress ; Weiss & Chene, 2020)
Martin, M. M., & Rubin, R. B. (1995). A New Measure of Cognitive Flexibility. Psychological Reports, 76(2), 623‑626. https://doi.org/10.2466/pr0.1995.76.2.623
Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The Autism-Spectrum Quotient (AQ) : Evidence from Asperger Syndrome/High-Functioning Autism, Malesand Females, Scientists and Mathematicians. Journal of Autism and Developmental Disorders, 31(1), 5‑17.
Oreg, S. (2003). Resistance to change : Developing an individual differences measure. Journal of Applied Psychology, 88(4), 680‑693. https://doi.org/10.1037/0021-9010.88.4.680
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
[1] "To generate item for a scale, you can either build them yourself or get inspired by other scales. In either way, you first need to read a lot of scientific article on your subject. You then need to define your topic, what you want to measure, with what kind of Scale (likert, thurstone, ...) and how many item you want at the end of all analysis. It is recommanded to generate 10x more item than what you want, as many will be discarded during the validation process."
Q5 was really to hard to understand. We therefore removed it for futur analysis. We can also see that a 7 Likert item Scale isn’t suitable. There isn’t a lot of response for the middle choice (as we can see on thos histograms with the blue line). We couldnt delete other items by looking at the distribution, because the Likert Scale Wasn’t good enough. We choose to dial back and take a 6-point Likert Scale, to force a polarity answer and avoid the middle choice pit problem.
During this step, we changed the 7-point Likert Scale to a 6-point Likert scale. We also removed the Q5 item of the pretest, as it was labelled as “not understandable”. Therefore, the Q5 item in the new questionnaire doesn’t refer to the same sentence. We also put 3 other scales (resistance to change subscale, autisme quotient subscale, cognitive flexibility scale), and added at the end an url to a numerised WCST task.
nflex (alias GCF) scale should be highly correlated with the WCST. Resistance to change scale should be negatively correlated, autism quotient subscale should be positively correlated and cognitive flexibility scale shoudl not be correlated. We made thos assumptions based on scientific litterature (neuropsychological report, social psychologic reports, cognitive psychologic report) using pubMed and PsychInfo database. We also expect to have a 1-factor structur but need to use EFA instead of CFA, as nobody validated the nflex scale before.
| scale | TLI | RMSEA | SRMR | Chi2 | p.value | GFI | AGFI | CFI |
|---|---|---|---|---|---|---|---|---|
| CFS | 0.8671831 | 0.1159691 | 0.0931220 | 307.4565 | 0.0000000 | 0.9680991 | 0.9261553 | 0.8913316 |
| AQ | 0.5884802 | 0.1332097 | 0.1177515 | 251.7531 | 0.0000000 | 0.9414338 | 0.8745010 | 0.6799291 |
| RTC | 0.9337185 | 0.1505368 | 0.0541501 | 17.8176 | 0.0001352 | 0.9948545 | 0.9331087 | 0.9779062 |
The same criterion as the EFA are used. The TLI index must be higher than .95, the RMSEA must be lower than .10, the SRMR must be lower than .08, the chi2 should be non significant, the GFI index should be higher than .95, the AGFI index must be higher than .90 and the CFI index must be higher than .90
REMINDER : All the items of each scales are fitted on 1 factor, as recommanded by the authors.This page shows the raw datatable named data that contains the data used for the analysis, the descriptive statistics of this table, and some graphics that would help for a more indepth comprehension of the sample and its specificity.
| value | frequency |
|---|---|
| Autre | 2 |
| Femme | 274 |
| Homme | 111 |
| value | frequency |
|---|---|
| Etudiant | 161 |
| Sans emploi | 37 |
| Travailleur | 189 |
This page shows histograms of the 4 scales we used in our research. We analysed the General Cognitive Flexibility Scale to check if any items needed to be removed. Conclusion of this analysis is stated below :
| item | skewness | kurtosis |
|---|---|---|
| nflex_1 | -0.57 | 3.02 |
| nflex_2 | -0.01 | 2.34 |
| nflex_3 | 0.09 | 2.41 |
| nflex_4 | -0.38 | 2.80 |
| nflex_5 | -0.22 | 2.47 |
| nflex_6 | -0.23 | 2.25 |
| nflex_7 | 0.05 | 2.47 |
| nflex_8 | -0.34 | 2.81 |
| nflex_9 | 0.15 | 2.37 |
| nflex_10 | -0.40 | 2.55 |
| item | skewness | kurtosis |
|---|---|---|
| att_switch_1 | 0.52 | 3.11 |
| att_switch_2 | 0.00 | 2.40 |
| att_switch_3 | -0.54 | 2.73 |
| att_switch_4 | 0.11 | 2.12 |
| att_switch_5 | -0.35 | 2.30 |
| att_switch_6 | -0.56 | 3.02 |
| att_switch_7 | -0.51 | 2.73 |
| att_switch_8 | -0.46 | 2.69 |
| att_switch_9 | 0.40 | 2.38 |
| att_switch_10 | 0.12 | 2.15 |
| item | skewness | kurtosis |
|---|---|---|
| cognitive_rigidity_1 | -0.36 | 2.81 |
| cognitive_rigidity_2 | 0.02 | 2.59 |
| cognitive_rigidity_3 | 0.00 | 2.74 |
| cognitive_rigidity_4 | -0.33 | 3.18 |
| item | skewness | kurtosis |
|---|---|---|
| cfs_1 | -0.40 | 2.63 |
| cfs_2 | -0.31 | 2.61 |
| cfs_3 | -0.54 | 2.58 |
| cfs_4 | -0.21 | 3.22 |
| cfs_5 | -0.43 | 2.81 |
| cfs_6 | -0.32 | 2.51 |
| cfs_7 | -0.14 | 3.23 |
| cfs_8 | -0.24 | 3.02 |
| cfs_9 | -0.08 | 3.12 |
| cfs_10 | -0.53 | 2.99 |
| cfs_11 | -0.69 | 3.13 |
| cfs_12 | -0.23 | 2.80 |
We used the parallel analysis with polychoric correlation because we have ordinal data, skewed items and non normality.We then used the efa with least squares algorithm (principal axis factor analysis), with number of factor indicated by the parallel analysis, and a varimax rotation. We set the factor loadings limit to .40 (Peterson, 2000). Anything below is considered too small.
| nflex_2 | nflex_3 | nflex_5 | nflex_7 | nflex_8 | nflex_9 | |
|---|---|---|---|---|---|---|
| nflex_2 | 1.0000000 | |||||
| nflex_3 | -0.2200531 | 1.0000000 | ||||
| nflex_5 | -0.1643719 | 0.2425024 | 1.0000000 | |||
| nflex_7 | -0.2792334 | 0.3078536 | 0.2796122 | 1.0000000 | ||
| nflex_8 | 0.2160019 | -0.0335953 | 0.0472469 | -0.1530191 | 1.0000000 | |
| nflex_9 | -0.0555067 | 0.1449438 | 0.0841761 | 0.4194474 | -0.1042351 | 1 |
| TLI | RMSEA | SRMR | BIC | Chi2 | p.value | |
|---|---|---|---|---|---|---|
| RMSEA | 1.040851 | 0.0000000 | 0.0187957 | -11.27563 | 0.4945884 | 0.7809109 |
| lower | 0.0000000 | |||||
| upper | 0.0662668 | |||||
| confidence | 0.9000000 |
| Loadings | Communalities | R² | |
|---|---|---|---|
| nflex_2 | -0.45 | 0.21 | 0.10 |
| nflex_3 | 0.53 | 0.28 | 0.13 |
| nflex_5 | 0.44 | 0.20 | 0.10 |
| nflex_7 | 0.63 | 0.40 | 0.16 |
| nflex_2 | nflex_3 | nflex_5 | nflex_7 | |
|---|---|---|---|---|
| nflex_2 | 0.79 | 0.00 | 0.02 | -0.01 |
| nflex_3 | 0.00 | 0.72 | 0.01 | -0.01 |
| nflex_5 | 0.02 | 0.01 | 0.80 | 0.00 |
| nflex_7 | -0.01 | -0.01 | 0.00 | 0.60 |
| alpha | Omega | |
|---|---|---|
| nflex reliability | 0.57 | 0.57 |
| TLI | RMSEA | SRMR | Chi2 | p.value | GFI | AGFI | CFI |
|---|---|---|---|---|---|---|---|
| 0.8671831 | 0.1159691 | 0.093122 | 307.4565 | 0 | 0.9680991 | 0.9261553 | 0.8913316 |
| 0.1035497 | |||||||
| 0.1287402 |
| TLI | RMSEA | SRMR | Chi2 | p.value | GFI | AGFI | CFI |
|---|---|---|---|---|---|---|---|
| 0.5884802 | 0.1332097 | 0.1177515 | 251.7531 | 0 | 0.9414338 | 0.874501 | 0.6799291 |
| 0.1180058 | |||||||
| 0.1489220 |
| TLI | RMSEA | SRMR | Chi2 | p.value | GFI | AGFI | CFI |
|---|---|---|---|---|---|---|---|
| 0.9337185 | 0.1505368 | 0.0541501 | 17.8176 | 0.0001352 | 0.9948545 | 0.9331087 | 0.9779062 |
| 0.0915726 | |||||||
| 0.2181803 |